Breast Cancer Prediction Using Fine Needle Aspiration Features and Upsampling with Supervised Machine Learning
Breast cancer is one of the most common invasive cancers in women and it continues to be a worldwide medical problem since the number of cases has significantly increased over the past decade. Breast cancer is the second leading cause of death from cancer in women. The early detection of breast canc...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/15/3/681 |
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| author | Rahman Shafique Furqan Rustam Gyu Sang Choi Isabel de la Torre Díez Arif Mahmood Vivian Lipari Carmen Lili Rodríguez Velasco Imran Ashraf |
| author_facet | Rahman Shafique Furqan Rustam Gyu Sang Choi Isabel de la Torre Díez Arif Mahmood Vivian Lipari Carmen Lili Rodríguez Velasco Imran Ashraf |
| author_sort | Rahman Shafique |
| collection | DOAJ |
| description | Breast cancer is one of the most common invasive cancers in women and it continues to be a worldwide medical problem since the number of cases has significantly increased over the past decade. Breast cancer is the second leading cause of death from cancer in women. The early detection of breast cancer can save human life but the traditional approach for detecting breast cancer disease needs various laboratory tests involving medical experts. To reduce human error and speed up breast cancer detection, an automatic system is required that would perform the diagnosis accurately and timely. Despite the research efforts for automated systems for cancer detection, a wide gap exists between the desired and provided accuracy of current approaches. To overcome this issue, this research proposes an approach for breast cancer prediction by selecting the best fine needle aspiration features. To enhance the prediction accuracy, several feature selection techniques are applied to analyze their efficacy, such as principal component analysis, singular vector decomposition, and chi-square (Chi2). Extensive experiments are performed with different features and different set sizes of features to investigate the optimal feature set. Additionally, the influence of imbalanced and balanced data using the SMOTE approach is investigated. Six classifiers including random forest, support vector machine, gradient boosting machine, logistic regression, multilayer perceptron, and K-nearest neighbors (KNN) are tuned to achieve increased classification accuracy. Results indicate that KNN outperforms all other classifiers on the used dataset with 20 features using SVD and with the 15 most important features using a PCA with a 100% accuracy score. |
| first_indexed | 2024-03-11T09:50:52Z |
| format | Article |
| id | doaj.art-3ac96de7b3d94bb8ac5a158efc2ef83a |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-11T09:50:52Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-3ac96de7b3d94bb8ac5a158efc2ef83a2023-11-16T16:15:43ZengMDPI AGCancers2072-66942023-01-0115368110.3390/cancers15030681Breast Cancer Prediction Using Fine Needle Aspiration Features and Upsampling with Supervised Machine LearningRahman Shafique0Furqan Rustam1Gyu Sang Choi2Isabel de la Torre Díez3Arif Mahmood4Vivian Lipari5Carmen Lili Rodríguez Velasco6Imran Ashraf7Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of KoreaSchool of Computer Science, University College Dublin, D04 V1W8 Dublin, IrelandDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of KoreaDepartment of Signal Theory and Communications and Telematic Engineering, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, SpainDepartment of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur 63100, Punjab, PakistanResearch Group on Foods, Nutritional Biochemistry and Health, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, SpainResearch Group on Foods, Nutritional Biochemistry and Health, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, SpainDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of KoreaBreast cancer is one of the most common invasive cancers in women and it continues to be a worldwide medical problem since the number of cases has significantly increased over the past decade. Breast cancer is the second leading cause of death from cancer in women. The early detection of breast cancer can save human life but the traditional approach for detecting breast cancer disease needs various laboratory tests involving medical experts. To reduce human error and speed up breast cancer detection, an automatic system is required that would perform the diagnosis accurately and timely. Despite the research efforts for automated systems for cancer detection, a wide gap exists between the desired and provided accuracy of current approaches. To overcome this issue, this research proposes an approach for breast cancer prediction by selecting the best fine needle aspiration features. To enhance the prediction accuracy, several feature selection techniques are applied to analyze their efficacy, such as principal component analysis, singular vector decomposition, and chi-square (Chi2). Extensive experiments are performed with different features and different set sizes of features to investigate the optimal feature set. Additionally, the influence of imbalanced and balanced data using the SMOTE approach is investigated. Six classifiers including random forest, support vector machine, gradient boosting machine, logistic regression, multilayer perceptron, and K-nearest neighbors (KNN) are tuned to achieve increased classification accuracy. Results indicate that KNN outperforms all other classifiers on the used dataset with 20 features using SVD and with the 15 most important features using a PCA with a 100% accuracy score.https://www.mdpi.com/2072-6694/15/3/681breast cancer predictionfeature selectionfine-needle aspiration featuresprincipal component analysissingular value decompositiondeep learning |
| spellingShingle | Rahman Shafique Furqan Rustam Gyu Sang Choi Isabel de la Torre Díez Arif Mahmood Vivian Lipari Carmen Lili Rodríguez Velasco Imran Ashraf Breast Cancer Prediction Using Fine Needle Aspiration Features and Upsampling with Supervised Machine Learning Cancers breast cancer prediction feature selection fine-needle aspiration features principal component analysis singular value decomposition deep learning |
| title | Breast Cancer Prediction Using Fine Needle Aspiration Features and Upsampling with Supervised Machine Learning |
| title_full | Breast Cancer Prediction Using Fine Needle Aspiration Features and Upsampling with Supervised Machine Learning |
| title_fullStr | Breast Cancer Prediction Using Fine Needle Aspiration Features and Upsampling with Supervised Machine Learning |
| title_full_unstemmed | Breast Cancer Prediction Using Fine Needle Aspiration Features and Upsampling with Supervised Machine Learning |
| title_short | Breast Cancer Prediction Using Fine Needle Aspiration Features and Upsampling with Supervised Machine Learning |
| title_sort | breast cancer prediction using fine needle aspiration features and upsampling with supervised machine learning |
| topic | breast cancer prediction feature selection fine-needle aspiration features principal component analysis singular value decomposition deep learning |
| url | https://www.mdpi.com/2072-6694/15/3/681 |
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